What are Learning Analytics?

Learning analytics refers to the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues. Data are collected from explicit student actions, such as completing assignments and taking exams, and from tacit actions, including online social interactions, extracurricular activities, posts on discussion forums, and other activities that are not directly assessed as part of the student’s educational progress. Analysis models that process and display the data assist faculty members and school personnel in interpretation. The goal of learning analytics is to enable teachers and schools to tailor educational opportunities to each student’s level of need and ability.

Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses across the country and leverages the vast amount of data produced by students in day-to-day academic activities.

While learning analytics has already been used in admissions and fund-raising efforts on several campuses, “academic analytics” is just beginning to take shape. Learning analytics need not simply focus on student performance. It might be used as well to assess curricula, programs, and institutions. It could contribute to existing assessment efforts on a campus, helping provide a deeper analysis, or it might be used to transform pedagogy in a more radical manner. It might also be used by students themselves, creating opportunities for holistic synthesis across both formal and informal learning activities.

ALT-C Next Steps:

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How can we maximise the ability of Higher and Further Education institutions and their learning technology innovators to take advantage of this emerging technology and its applications?

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Work of the Advisory Board previous to Sept 5

INSTRUCTIONS: Enter your responses to the questions below. This is most easily done by moving your cursor to the end of the last item and pressing RETURN to create a new bullet point. Please include URLs whenever you can (full URLs will automatically be turned into hyperlinks; please type them out rather than using the linking tools in the toolbar).

Please "sign" your contributions by marking with the code of 4 tildes (~) in a row so that we can follow up with you if we need additional information or leads to examples- this produces a signature when the page is updated, like this: - Larry Larry Jan 25, 2011

(1) How might this technology be relevant to the educational sector you know best?

  • Data already used for forecasting/predicting student numbers. Implication of LA as often imagined is that it will be used to "optimise" learning in some way, though what that means is moot... - tony.hirst tony.hirst May 16, 2011
  • Four uses I think: i. Student progress and monitoring – generally this is to allow intervention; ii) Course design – are resources being used, and are there areas where students get stuck, seek advice or drop out; iii) Learner empowerment – visualising the data and feeding it back to the learner themselves to help direct their own learning; iv) Personalisation – recommending or providing resources, people, tasks, etc tailored to the individual, based on analysis of their data, and mining large data sets.- martin.weller martin.weller May 16, 2011

(2) What themes are missing from the above description that you think are important?

  • I think there is a whole area relating to the way the HEIs use data, from forecasting and estate management, to personal progress monitoring (maybe made available to students, rather than staff?), course activity monitoring, automated interventions (calendar alerts, textmining forums, in response to online quizzes/assessment or grade tracking/perfromance prediction). One thing data allows us to do is run experiments/trials, and test outcomes of different sorts of intervientions, I suspect the many HEIs will be averse to this approach... - tony.hirst tony.hirst May 16, 2011
  • A distinction, in course related analytics, between reports that students/staff use to monitor progress of students, vs analytics around the performance of the course itself, how people utilise/access resources, create unanticipated pathways through the course etc - tony.hirst tony.hirst May 16, 2011
  • Some of the reticence, suspicion around learning analytics? Many people are wary of it, partly from a 'Big Brother' concern and also as it may feed into the corporatisation/commercialisation of education and reduces everything to a measurement. - martin.weller martin.weller May 16, 2011
  • Important to consider analytics more holistically and not in isolation - this will mean linking to systems which may not be directly related to learning e.g. pastoral support, fees/debt, presence/access control, attendance, library use etc. - chris.cobb chris.cobb May 17, 2011
  • This description conflates two important issues - 1. the better collation and analysis of data on general patterns of activity that institutions already have in order to improve efficiency and 2. the better recording and analysis of data as a pedagogic tool by students as a basis for collaboration with educators and peers on their learning. We need to address the urgent issue of 'ownership' of this data, and how students themselves can generate and share it according to their priorities and needs. The data analytics argument requires a radical rethink of who we think the 'users' of the data are. Looking at the recent NHS records fiasco, it is also likely that centralised systems of data ownership, where the institution is seen as the repository rather than the individual, may be untenable. - keri.facer keri.facer May 18, 2011

(3) What do you see as the potential impact of this technology on teaching, learning, research or information management within the next five years?

  • I would like to see analytics being used to improve the performance/design of online courses as web properties - tony.hirst tony.hirst May 16, 2011
  • Three impacts I think: i. Improved student retention – by intervening at strategic points, before an issue has become too serious, students can be kept in full time study. This can be allied to market segmentation eg do different segments have different retention patterns? ii) Improved efficiency in course design – knowing how resources are used and which ones relate directly to student performance can help improve course design and allocation or resources, and also acts as a test on the intended design. iii) Greater student independence – analytics that are used to empower the learner can alleviate some of the demand on ALs and central staff to provide support. - martin.weller martin.weller May 16, 2011

(4) Do you have or know of a project working in this area?

Please share information about related projects in our Horizon Project sharing form.